深度学习 · graph neural networks论文趋势 2023–2026

主要结论

• 深度学习 · graph neural networks论文从 2023 年的 728 篇降到 2026 年的 326 篇，热度在回落。
• NeurIPS 是发深度学习 · graph neural networks论文最多的会议，2023–2026 年共 842 篇，占四个会议总量的 51%。
• 交叉方向里涨得最快的是数据集与基准：同时属于两个主题的论文从 2023 年的 9 篇增至 2026 年的 20 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 5；同组增长最快的扩散模型是 4.1 倍，深度学习 · graph neural networks是 0.4 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026A New Perspective on Target-Conditioned Structural Dynamics for Link Prediction in Dynamic Graphs
• ICML 2026A Graph Foundation Model with Cross-Modal Alignment and Modality-Aware Expert Fusion for Multi-Modal Graphs
• ICLR 2026Laplacian Kernelized Bandit
• CVPR 2026Gamba Mamba-based graph convolutional network with dynamic graph topology learning
• NeurIPS 2025Depth-Width Tradeoffs for Transformers on Graph Tasks
• ICML 2025A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks
• ICLR 2025Factor Graph-based Interpretable Neural Networks
• CVPR 2025Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
• NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
• ICML 2024A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
• ICLR 2024A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
• CVPR 2024Learning Structure-from-Motion with Graph Attention Networks
• NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
• ICML 2023A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
• ICLR 2023Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
• CVPR 2023An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity
• NeurIPS 2026A Topological Encoder Decoder Framework for Temporal Graph Learning
• ICML 2026A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
• ICLR 2026GTool: Graph Enhanced Tool Planning with Large Language Model
• CVPR 2026GSNR Graph Smooth Null-Space Representation for Inverse Problems

全部深度学习 · graph neural networks论文

共 533 篇，第 11 / 11 页

501. ICML 2023Revisiting Weighted Aggregation in Federated Learning with Neural Networks
502. ICML 2023RGE: A Repulsive Graph Rectification for Node Classification via Influence
503. ICML 2023RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations
504. ICML 2023Scaling Spherical CNNs
505. ICML 2023Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
506. ICML 2023SeedGNN: Graph Neural Network for Supervised Seeded Graph Matching
507. ICML 2023SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
508. ICML 2023SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
509. ICML 2023SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge
510. ICML 2023Spatial Implicit Neural Representations for Global-Scale Species Mapping
511. ICML 2023Special Properties of Gradient Descent with Large Learning Rates
512. ICML 2023spred: Solving L1 Penalty with SGD
513. ICML 2023Statistical Foundations of Prior-Data Fitted Networks
514. ICML 2023StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
515. ICML 2023Subequivariant Graph Reinforcement Learning in 3D Environments
516. ICML 2023Taming graph kernels with random features
517. ICML 2023The Acquisition of Physical Knowledge in Generative Neural Networks
518. ICML 2023The Hessian perspective into the Nature of Convolutional Neural Networks
519. ICML 2023Total Variation Graph Neural Networks
520. ICML 2023Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
521. ICML 2023Towards Deep Attention in Graph Neural Networks: Problems and Remedies
522. ICML 2023Towards Robust Graph Incremental Learning on Evolving Graphs
523. ICML 2023Towards Understanding Generalization of Graph Neural Networks
524. ICML 2023Trompt: Towards a Better Deep Neural Network for Tabular Data
525. ICML 2023Under-Counted Tensor Completion with Neural Incorporation of Attributes
526. ICML 2023Understanding Oversquashing in GNNs through the Lens of Effective Resistance
527. ICML 2023Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data
528. ICML 2023Vertical Federated Graph Neural Network for Recommender System
529. ICML 2023Wasserstein Barycenter Matching for Graph Size Generalization of Message Passing Neural Networks
530. ICML 2023What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings
531. ICML 2023When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations
532. ICML 2023Width and Depth Limits Commute in Residual Networks
533. ICML 2023XAI Beyond Classification: Interpretable Neural Clustering

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · graph neural networks论文？

共 1,648 篇，按年份分别是 2023 年 728 篇、2024 年 297 篇、2025 年 297 篇、2026 年 326 篇。

NeurIPS 2026 有多少篇深度学习 · graph neural networks论文？

NeurIPS 2026 有 102 篇深度学习 · graph neural networks论文，上一届是 91 篇。

ICLR 2026 有多少篇深度学习 · graph neural networks论文？

ICLR 2026 有 32 篇深度学习 · graph neural networks论文，上一届是 50 篇。

ICML 2026 有多少篇深度学习 · graph neural networks论文？

ICML 2026 有 168 篇深度学习 · graph neural networks论文，上一届是 141 篇。

CVPR 2026 有多少篇深度学习 · graph neural networks论文？

CVPR 2026 有 24 篇深度学习 · graph neural networks论文，上一届是 15 篇。

哪个会议的深度学习 · graph neural networks论文最多？

NeurIPS，2023–2026 年共 842 篇。